Camera Platform and Object Inventory Control

ABSTRACT

Camera platform techniques are described. In an implementation, a plurality of digital images and data describing times, at which, the plurality of digital images are captured is received by a computing device. Objects of clothing are recognized from the digital images by the computing device using object recognition as part of machine learning. A user schedule is also received by the computing device that describes user appointments and times, at which, the appointments are scheduled. A user profile is generated by the computing device by training a model using machine learning based on the recognized objects of clothing, times at which corresponding digital images are captured, and the user schedule. From the user profile, a recommendation is generated by processing a subsequent user schedule using the model as part of machine learning by the computing device.

RELATED APPLICATIONS

This application claims priority under 35 U.S.C. § 120 to applicationSer. No. 16/673,638, filed Nov. 4, 2019, which claims priority under 35U.S.C. § 120 to application Ser. No. 15/858,463, filed Dec. 29, 2017,which claims priority under 35 U.S.C. § 119(e) to U.S. ProvisionalPatent Application No. 62/558,836, filed Sep. 14, 2017, and titled“Camera Platform and Object Inventory Control”, the disclosures of whichare incorporated by reference herein.

BACKGROUND

Mobile devices have become an integral part of a user's everyday life. Amobile phone, for instance, may be used to read emails, engage in socialmedia, capture digital images, communicate via instant messages, and soforth. Likewise, wearable devices such as smart watches have continuedto expand this interaction. Accordingly, users have access to a widerange of devices in a variety of usage scenarios.

However, configuration as a mobile device may introduce challenges andcomplexity in support of user interactions with these devices. A mobilephone or smart watch, for instance, may have a limited ability tosupport entry of text, navigate between files, and so on. Accordingly,user interaction with these devices may be limited and causecomputational inefficiencies as a result.

SUMMARY

Camera platform and object inventory control techniques are described.In an implementation, a plurality of digital images and data describingtimes, at which, the plurality of digital images are captured isreceived by a computing device. Objects of clothing are recognized fromthe digital images by the computing device using object recognition aspart of machine learning. A user schedule is also received by thecomputing device that describes user appointments and times, at which,the appointments are scheduled. A user profile is generated by thecomputing device by training a model using machine learning based on therecognized objects of clothing, times at which corresponding digitalimages are captured, and the user schedule. From the user profile, arecommendation is generated by processing a subsequent user scheduleusing the model as part of machine learning by the computing device,which is output is a user interface.

This Summary introduces a selection of concepts in a simplified formthat are further described below in the Detailed Description. As such,this Summary is not intended to identify essential features of theclaimed subject matter, nor is it intended to be used as an aid indetermining the scope of the claimed subject matter.

BRIEF DESCRIPTION OF THE DRAWINGS

The detailed description is described with reference to the accompanyingfigures. Entities represented in the figures may be indicative of one ormore entities and thus reference may be made interchangeably to singleor plural forms of the entities in the discussion.

FIG. 1 is an illustration of an environment in an example implementationthat is operable to employ camera platform techniques described herein.

FIG. 2 depicts a system in an example implementation showing operationof a camera platform manager module of FIG. 1 in greater detail.

FIG. 3 depicts examples of user interaction with the camera platformmanager module as shown using first, second, and third stages to collectmetadata related to an object recognized in a digital image.

FIG. 4 depicts an example of stored results of recognized objects andcorresponding metadata and user-definable collections of the objects.

FIG. 5 depicts an example implementation of a user interface configuredto support purchase and sale of objects recognized using a cameraplatform manager module of FIG. 1.

FIG. 6 also depicts implementation of options that are output forpurchase or sale of an object recognized from a digital image using thecamera platform manager module.

FIG. 7 depicts a system in an example implementation showing operationof the camera platform manager module of FIG. 1 in greater detail asemploying a user profile.

FIG. 8 depicts an example implementation of generation of the userprofile based at least in part on machine learning.

FIG. 9 depicts an example implementation of generation of the userprofile based at least in part on stature data.

FIG. 10 depicts an example system of obtaining a recommendation based onthe generated user profile of FIG. 8.

FIG. 11 depicts an example implementation in which a user schedule isemployed along with digital images to generate a user profile usingmachine learning that is used as a basis to generate a recommendation.

FIG. 12 is a flow diagram depicting a procedure in an exampleimplementation in which a user schedule and digital images are used totrain a model using machine learning to generate user recommendations.

FIG. 13 illustrates an example system including various components of anexample device that can be implemented as any type of computing deviceas described and/or utilize with reference to FIGS. 1-12 to implementembodiments of the techniques described herein.

DETAILED DESCRIPTION

In the following discussion, an example environment is first describedthat may employ the techniques described herein. Example procedures andsystems are also described and shown as blocks which may be performed inthe example environment as well as other environments. Consequently,performance of the example procedures is not limited to the exampleenvironment and systems and the example environment and systems are notlimited to performance of the example procedures.

Example Environment

FIG. 1 is an illustration of a digital medium environment 100 in anexample implementation that is operable to employ digital image capturesession techniques described herein. The illustrated environment 100includes a computing device 102 that is communicatively coupled to aservice provider system 104 via a network 106. Computing devices thatimplement the computing device 102 and the service provider system 104may be configured in a variety of ways.

A computing device, for instance, may be configured as a desktopcomputer, a laptop computer, a mobile device (e.g., assuming a handheldconfiguration such as a tablet or mobile phone), configured to be worn(e.g., as goggles as illustrated for computing device 102) and so forth.Thus, a computing device may range from full resource devices withsubstantial memory and processor resources (e.g., personal computers,game consoles) to a low-resource device with limited memory and/orprocessing resources (e.g., mobile devices). Additionally, although asingle computing device is shown, a computing device may berepresentative of a plurality of different devices, such as multipleservers utilized by a business to perform operations “over the cloud”for the service provider system 104 as described in FIG. 13.

The computing device 102 is illustrated as being worn by a user 108 in aphysical environment, e.g., a living room 110. The computing device 102includes a digital camera 112 that is configured to capture digitalimages 114 of an outside physical environment (e.g., the living room106), such as through use of a charge coupled device (CCD) sensor. Thecaptured digital images 114 may then be stored as pixels in acomputer-readable storage medium and/or rendered for display by adisplay device, e.g., LCD, OLED, LED, etc.

The computing device 102 also includes a camera platform manager module116 that is configured to implement and execute a camera platform 118(e.g., through use of a processing system and computer-readable storagemedia) that may serve as a basis for a variety of functionality. Thecamera platform 118, for instance, may implement a “live view” formed ofdigital images 114 taken of the physical environment of the computingdevice 102, i.e., a real time output. These digital images 114 may thenserve as a basis to support other functionality.

An example of this functionality is illustrated as an object inventorymanager module 120. The object inventory manager module 120 isrepresentative of functionality to manage an inventory of objects. Thismay include objects that are owned by the user 108 and/or objects thatare desired by the user 108, e.g., for purchase. This may be implementedby the object inventory manager module 120 through use of the cameraplatform 118 in a variety of ways.

In a first such example, the object inventory manager module 120 isconfigured to collect digital images 114. This may include digitalimages 114 of physical objects in the living room 110 in this example ordigital images captured of physical photos, e.g., from a magazine, apicture taken of a television screen or other display device, and so on.The digital image 114 may also be captured of a user interface output bythe computing device 102, e.g., as a screenshot from a frame buffer.

The object inventory manager module 120 includes object recognitionfunctionality to recognize objects included within the digital image114, e.g., via machine learning. From this, the object inventory managermodule 120 may collect data pertaining to this recognition. Datadescribing the recognized objects, for instance, may be communicated viathe network 106 to the service provider system 104. The service providersystem 104 includes a service manager module 122 that is configured toobtain data related to the objects (e.g., through use of a search) froma storage device 124. This data may then be communicated back to thecomputing device 102 via the network 106 for use by the object inventorymanager module 120.

The object inventory manager module 120, for instance, may generateaugmented reality digital content 126 (illustrated as stored in astorage device 128) for output in the user interface of the computingdevice 102 as part of a “live feed” of digital images taken of thephysical environment, e.g., the living room in real time. The AR digitalcontent 126, for instance, may describe characteristics of the object, abrand name of the object, a price for which the object is available forsale or purchase (e.g., via an online auction), and so forth. This ARdigital content 126 is then displayed proximal to the object by theobject inventory manager module 120. In this way, the camera platformsupports functionality for the user 108 to “look around” the living room110 and object additional information and insight into characteristicsof objects included within the physical environment. Further discussionof this example is described in relation to FIGS. 2-6 in the followingdiscussion.

In another example, the object inventory manager module 120 leveragesthe camera platform 118 to make recommendations for a user. The digitalimage 114, for instance, may also be processed by the object inventorymanager module using object recognition as implemented using machinelearning. In this example, the digital images are used to generate aprofile (e.g., a user profile) based on characteristics learned from thedigital images 114, e.g., to train a model. This profile is then used asa basis to form recommendations (e.g., through machine learning), suchas to configure digital marketing content having product suggestionsbased on these characteristics.

The profile, for instance, may be based on digital images taken of theuser 108. From this, the object inventory manager module 120 maydetermine a likely size (e.g., dimensions) of the user, stature (e.g.,how a user wears clothing such as tight, loose, or otherwise how theclothing “hangs” on the user), style (e.g., professional, hippy, grunge,medieval), and so forth. Digital images may also be collected fromsources that do not include the user but are desired by the user, e.g.,of other humans in person, from physical photos, and so forth.

From this, the object inventory manager module 120 may generaterecommendations based on the user profile, such as to generate digitalmarketing content for products or services based on the size, stature,and style described in the user profile. In this way, the objectrecognition module may increase accuracy and as a result increasecomputational efficiency in generation of recommendations based on thecamera platform 118. This example is further described in relation toFIGS. 7-12 in the following description.

In general, functionality, features, and concepts described in relationto the examples above and below may be employed in the context of theexample procedures described in this section. Further, functionality,features, and concepts described in relation to different figures andexamples in this document may be interchanged among one another and arenot limited to implementation in the context of a particular figure orprocedure. Moreover, blocks associated with different representativeprocedures and corresponding figures herein may be applied togetherand/or combined in different ways. Thus, individual functionality,features, and concepts described in relation to different exampleenvironments, devices, components, figures, and procedures herein may beused in any suitable combinations and are not limited to the particularcombinations represented by the enumerated examples in this description.

Camera Platform and Object Inventory Control

FIG. 2 depicts a system 200 in an example implementation showingoperation of the camera platform manager module 116 of FIG. 1 in greaterdetail. FIG. 3 depicts examples of user interaction with the cameraplatform manager module 120 as shown using first, second, and thirdstages 302-306 to collect metadata related to an object recognized in adigital image. FIG. 4 depicts an example of stored results of recognizedobjects and corresponding metadata and user-definable collections of theobjects. FIG. 5 depicts an example implementation of a user interfaceconfigured to support purchase and sale of objects recognized using thecamera platform manager module 116. FIG. 6 also depicts implementationof options output for purchase or sale of an object recognized from adigital image using the camera platform manager module.

The following discussion describes techniques that may be implementedutilizing the previously described systems and devices. Aspects of theprocedure as shown stepwise by the modules of FIG. 2 may be implementedin hardware, firmware, software, or a combination thereof. The procedureis shown as a set of blocks that specify operations performed by one ormore devices and are not necessarily limited to the orders shown forperforming the operations by the respective blocks. In portions of thefollowing discussion, reference will be made to FIGS. 2-6.

To begin, a digital image 114 is obtained by the camera platform managermodule 116. The digital image 114, for instance, may be captured using adigital camera, as a screenshot captured from a frame buffer of thecomputing device 102, and so forth.

The digital image 114 is then processed by an object recognition module202 to recognize an object within the digital image 114. The objectrecognition module 202, for instance, may employ a machine learningmodule 204 configured to employ models 206 usable to recognize theobject using machine learning, e.g., neural networks, convolutionalneural networks, deep learning networks, structured vector machines,decision trees, and so forth. The models 206, for instance, may betrained using digital images that are tagged with correspondingidentifications. In an implementation, these digital images and tags areobtained from a commerce service provider system that are tagged bysellers using the system. As a result, a multitude of accurately taggeddigital images may be obtained with minimal computation and user cost asopposed to conventional manual tagging techniques. Although illustratedas implemented locally by the computing device 102, this functionalitymay also be implemented in whole or in part by a service provider system104 via the network 106.

Thus, the object recognition data 208 describes an object included inthe digital image 114. An object data collection module 210 is thenemployed to collect object metadata 212 that pertains to the recognizedobject. This may be performed locally through a search of a localstorage device and/or remotely through interaction with a servicemanager module 122 of a service provider system 104 via a network 106.

A variety of different types of object metadata 212 may be obtained froma variety of different types of service provider systems 104. In oneexample, the service provider system 104 provides information relatingto purchase or sale of the object, e.g., product name, productdescription, price for purchase or sale (e.g., based on onlineauctions), and so forth. In another example, the service provider system104 provides information relating to customer reviews of the product,e.g., a number of “stars” or other rating, textual reviews, and soforth.

The object metadata 212 in this example is then provided to an augmentedreality (AR) configuration module 214. The AR configuration module 214,for instance, may be configured to generate AR digital content 126 fromthe object metadata 212 for display proximal to the object by an ARrendering module 216 to an output device 218, e.g., display device,audio output device, tactile (i.e., haptic) output device, and so forth.In order to generate haptic effects, many output devices utilize sometype of actuator or haptic output device. Examples of known hapticoutput devices used for this purpose include an electromagnetic actuatorsuch as an Eccentric Rotating Mass (“ERM”) in which an eccentric mass ismoved by a motor, a Linear Resonant Actuator (“LRA”) in which a massattached to a spring is driven back and forth, or a “smart material”such as piezoelectric, electro-active polymers or shape memory alloys.Haptic output devices also broadly include non-mechanical ornon-vibratory devices such as those that use electrostatic friction(ESF), ultrasonic surface friction (USF), or those that induce acousticradiation pressure with an ultrasonic haptic transducer, or those thatuse a haptic substrate and a flexible or deformable surface, or thosethat provide projected haptic output such as a puff of air using an airjet, and so on.

The augmented reality content in this example includes both contentsupported along with a direct view of a physical environment or contentsupported along with a recreated view of the physical environment. Inthis way, through use of a camera platform 118 as implemented by thecamera platform manager module 116, a user may simply “look around”using a live feed of digital images 114, select objects in the digitalimages 114, and obtain metadata related to the object, an example ofwhich is described as follows. This may also include an ability to showitems of clothing or accessories as part of a “live feed.”

FIG. 3 depicts an example implementation 300 of user interaction withthe camera platform 118 as implemented by the camera platform managermodule 116. This implementation 300 is illustrated using first, second,and third stages 302, 304, 306.

At the first stage 302, a user interface 308 is output by the outputdevice 218, e.g., a touchscreen display device. The user interface 308is configured as a “live feed” of digital images 114 obtained in realtime from the digital camera 112 in this example.

At the second stage 304, a user input is received that selects an objectdisplayed in the user interface 308. In the illustrated example, theuser input is detected as a tap of a finger of the user's hand 318 thatis detected using touchscreen functionality of the output device 218. Inanother example, a click-and-drag operation is performed to specify arectangular area in a user interface. In this way, a user maydistinguish between multiple objects displayed concurrently in the userinterface 308. Other examples are also contemplated, such as a spokenutterance or other gestures.

In response to the user selection of the second stage 304, the digitalimage 114 displayed in the user interface 308 is captured (e.g.,obtained from a frame buffer) along with the indication of the locationof the particular object, e.g., as guided by X/Y coordinates of the“tap.” The digital image 114 is then processed by the object recognitionmodule 202 as described above to identify the object (e.g., the pitcherin the illustrated example) as object recognition data 208.

The object recognition data 208 is then communicated to a serviceprovider system 104 in this example that is configured to supportpurchase and sale of goods. Accordingly, the service manager module 122in this example searches a storage device 124 for object metadata 212that pertains to the identified object. The object metadata 212 is thenconfigured by the AR configuration module 214 to generate AR digitalcontent 126 for output in the live/real time feed of digital images.

As shown at the third stage 306, examples of AR digital content 126include a name and price 310 (e.g., average price, price for sale, priceto buy, etc.) of the object, which is displayed proximal to the object,e.g., the pitcher. The AR rendering module 216 may then configure thiscontent to remain “as is” relative to the view of the object, e.g.,based on data received from orientation sensors such as accelerometers,inertial devices, gyroscope, image processing and feature recognitionfrom the digital images, and so forth.

The AR digital content 126 also includes information regarding the sameor similar goods 312, 314, 316 that are available for purchase from theservice provider system 104, e.g., as part of an online auction, for aset price, etc. In this way, the camera platform manager module 116implements the camera platform 118 as non-modal within the userinterface 308 such that a user remains within a context of a real time“live” feed of digital images and yet still is able to obtain metadatadescribing objects included in those digital images. The user 108, forinstance, may “look around” at different objects within the living room110 and readily determine how to buy or sell these objects based on realtime information obtained from the service provider system 104.

FIG. 4 depicts another example implementation 400 of the user interface306 that is configured to store and manage an inventory of the objectsrecognized using the previously described techniques. The user interface308 includes thumbnails of digital images 402, 404, 406 that werepreviously processed using the techniques described above and “stored”in response to user interaction with the user interface 306, e.g.,indicative of a future interest in respective objects included in thedigital images 402-406.

The object inventory manager module 120 is configured in this example toupdate metadata associated with these objects through communication withthe service provider system 104. The communication may follow a “push”model in response to changes in prices, a “pull” model as implemented atscheduled intervals or in response to access of this screen of the userinterface 308, and so forth.

The metadata in this example includes current respective names andprices 408, 410, 412, a user specified collection of the objects andassociated metadata 414 (e.g., a current value of the collection), aswell as an overall current value 416 for each of the saved objects.Thus, a user may form collections of saved objects as desired andinteract with these collections as desired, such as to generate anautomated listing together to sell the collection individually or as awhole.

FIG. 5 depicts an example implementation 500 of the user interface 308as configured to both buy and sell objects using saved objectedrecognized and processed by the camera platform. In this example, userselections are received to sell items corresponding to the pitcher andvase in digital images 402, 404 and buy another lamp corresponding todigital image 406. In response, the object inventory manager module 120collects metadata describing respective prices to sell 408, 410 and buy502 the objects. Metadata is also generated and output in the userinterface 308 describing an overall value of items being bought 504 aswell as an overall value of items being sold 506. An overall change invalue 508 is also output in the user interface 308.

A user, for instance, may desire to buy the additional lamp 502 andtherefore sell some objects to cover this cost. Through use of the savedobjects by the platform in this example, a user may quickly andefficiently determine how to make this happen, which is not possible inconventional techniques that could be cumbersome.

FIG. 6 depicts another example implementation 600 of use of a cameraplatform to aid purchase or sale of objects recognized in digital imagescollected from a digital camera. In this example, the user interface 308also displays a live feed of a digital image that includes an object ina physical environment, e.g., object itself, physical photograph of theobject, and so forth. In response, the object inventory manager module120 includes user selectable options to buy 602 or sell 604 to product,e.g., laundry detergent.

The object inventory manager module 120 also, through objectrecognition, recognizes that the object has been purchased before. Thismay be performed based on data local to the computing device 102 and/orthrough communication with the service provider system 104. In response,AR digital content 126 is rendered in the user interface 308 as anoption to “buy again” 606, to purchase a subscription at a reducedprice. Thus, in this example the user 108 may readily navigate through aphysical environment and purchase goods as needed in an efficient andintuitive manner.

Other examples are also contemplated. In a first such example, a usermay capture a digital image of an object, for which, an upgraded modelis not currently available but will be made available in the nearfuture. Thus, the metadata may describe availability of this new modelas well as options to sell the user's current model. In a second suchexample, the metadata is information, such as to indicate when to changea battery in a smoke detector, indicate cost saving that may be realizedby switching to an new version of a product (e.g., a LED light thatsaves 85% energy), and so forth.

Camera Platform and User Profiles

FIG. 7 depicts a system 700 in an example implementation showingoperation of the camera platform manager module 116 of FIG. 1 in greaterdetail as employing a user profile. FIG. 8 depicts an exampleimplementation 800 of generation of the user profile based at least inpart on machine learning. FIG. 9 depicts an example implementation 900of generation of stature data. FIG. 10 depicts an example system 1000 ofobtaining a recommendation based on the generated user profile of FIG.9. FIG. 11 depicts an example implementation 1100 involving use of alive feed and user schedule. FIG. 12 depicts a procedure 1200 in anexample implementation of training and use of a model using machinelearning based on object recognition and a user schedule.

The following discussion describes techniques that may be implementedutilizing the previously described systems and devices. Aspects of theprocedure as shown stepwise by the modules of FIGS. 8 and 9 and blocksof FIG. 12 may be implemented in hardware, firmware, software, or acombination thereof. The procedure is shown as a set of blocks thatspecify operations performed by one or more devices and are notnecessarily limited to the orders shown for performing the operations bythe respective blocks. In portions of the following discussion,reference will be made to FIGS. 7-12.

In this example, the object inventory manager module 120 leverages thecamera platform 118 to make recommendations for a user 108. The digitalimage 114, for instance, may also be processed by the object inventorymanager module 120 using object recognition as implemented using machinelearning. In this example, the digital images 114 are used to generate aprofile (e.g., a user profile 702) based on characteristics learned fromthe digital images 114, e.g., characteristics of objects such as clothesworn by a user. This profile 702 is then used as a basis to formrecommendations (e.g., through machine learning as further described inrelation to FIG. 8), such as to configure digital marketing contenthaving product suggestions based on these characteristics.

The profile, for instance, may be generated from digital images taken ofthe user 704. From this, the object inventory manager module 120 maydetermine a likely size (e.g., dimensions) of the user 704, stature(e.g., how a user wears clothing such as tight, loose, or otherwise howthe clothing “hangs” on the user), style (e.g., professional, hippy,grunge, medieval), and so forth. Digital images 114 may also becollected from sources that do not include the user but are desired bythe user, e.g., of other humans in person, from physical photos, and soforth.

From this, the object inventory manager module 120 may generaterecommendations based on the user profile, such as to generate digitalmarketing content for products or services based on the size, stature,and style described in the user profile. In this way, the objectrecognition module may increase accuracy and as a result increasecomputational efficiency in generation of recommendations based on thecamera platform 118.

FIG. 8 depicts an example 800 of generation of the user profile 702 ofFIG. 7 in greater detail. To begin, a digital image 114 is obtained bythe camera platform manager module 116 as before. The digital image 114,for instance, may be captured using a digital camera, as a screenshotcaptured from a frame buffer of the computing device 102, and so forth.

The digital image 114 is then processed by an object recognition module202 to recognize an object within the digital image 114. The objectrecognition module 202, for instance, may employ a machine learningmodule 204 configured to employ models 206 usable to recognize theobject using machine learning, e.g., neural networks. The models 206,for instance, may be trained using digital images that are tagged withcorresponding identifications. In an implementation, these digitalimages and tags are obtained from a commerce service provider systemthat are tagged by sellers using the system. That tags, for instance,may indicate a type of object, style of object, and so forth. As aresult, a multitude of digital images may be obtained for training withminimal computation and user cost as opposed to conventional manualtagging techniques. Although illustrated as implemented locally by thecomputing device 102, this functionality may also be implemented inwhole or in part by a service provider system 104 via the network 106.

Thus, the object recognition data 208 describes an object included inthe digital image 114. The machine learning module 204, andcorresponding models, are also trained to identify a likely size,stature, and style exhibited in the digital image 114 and thus outputsize data 802, stature data 804, and style data 806 as part of theobject recognition data 208. The size data 802 may include likelyoverall dimensions of the user 704, likely clothing sizes worn by theuser, and so forth.

The stature data 804 describes how the user 704 wears the clothes, e.g.,oversized, tight fitting, athletic, and so forth. The stature data 804,for instance, may be determined through a comparison of skeletaltracking of the user 704 (e.g., using a structured light camera ortime-of-flight camera) with overall size of the clothes, e.g., “how theclothing hangs on the user” such as baggy in the illustration.

As illustrated in the example implementation 900 of FIG. 9, forinstance, a digital image 114 may be received as previously described.From this digital image 114, skeletal tracking 902 is performed toindicate locations of joints of a user as captured in the digital image114 and distances between those joints, i.e., lengths of skeletalsegments. These distances are compared with size data 802 of the user inrelation to the object of clothing worn by the user. This comparisonresults in stature data 804 that describes a relationship between thesize of the clothing worn by the user and a size of the user and thusdescribes “how the object of clothing hangs” on the user, e.g., baggy,fitted, preference toward longer sleeves, and so forth. In this way,stature data 804 may go beyond merely describing whether an object ofclothing is likely to fit a user to describe how that fit is desired bythe user as exhibited by the digital image 114.

The style data 806 describes a style exhibited by the clothing of theuser 704 and/or the user 704, herself. The style data 806, for instance,may also be obtained as part of object recognition to identify not onlyan object included in a digital image but also a style exhibited by theobject. As previously described, for instance, style data 806 may betrained as part of a model based on tags of the digital images 114,e.g., by a commerce service provider system. Thus, style data 806 cantranscend different types of objects, e.g., for fashion this may includeformal, casual, modern, hipster, and so forth.

The digital image 114 may also be captured of other users that havefavorable characteristics, e.g., of a person on a street having adesired jacket, a digital image taken of a physical photograph of apopstar in a physical magazine, and so forth. Thus, this objectrecognition data 208 is collected by a profile data collection module808 to generate user profile data 810 that describes the user and/orcharacteristics of other users as recognized from the digital image 114.

As shown in FIG. 10, the user profile 702 may then be communicated tothe service provider system 104, which forms a recommendation 1002 basedon the user profile. The recommendation 1002, for instance, may begenerated using machine learning based on a user segment, to which, theuser belongs as identified through non-negative matrix factorization. Inthis way, the camera platform 118 of the object inventory manager module120 may address likely user desires based on the digital images andobject recognition supported by the platform. Although describes asimplemented by the computing device 102, this functionality may also beimplemented in whole or in part by the service provider system 104,e.g., in response to communicated images as part of a social networkservice.

FIG. 11 depicts an example implementation 1100 in which a user schedule1102 is employed along with digital images 114 to generate a userprofile 702 using machine learning that is used as a basis to generate arecommendation. In this example, digital images 1102 and associatedtimes, at which, those images are captured are received by the profiledata collection module 202. The digital images 1102, for instance, maybe captured over a period-of-time as part of a live camera feed asdescribed earlier, from a collection of still images, and so forth.

A user, for instance, may capture digital images that includes the useras well as objects of clothing worn by the user at different times ofday, days of the week, and so forth. A user schedule 1104 is alsoreceived 1104, through access granted to a user's calendar applicationas executed by the computing device. The user schedule 1104, forinstance, includes text that describes appointments and respective timesof the appointments of the user.

The digital images 1102 and user schedule 1104 are then used to train amodel 206 using machine learning. To do so, object recognition may beused as previously described to identify objects (e.g., using machinelearning) in the digital images 1102, which may also include size data802, stature data 804, and style data 806. The recognized objects andcharacteristics of those objects are then used along with the userschedule 1104 to train the model 206 using machine learning, e.g., aspart of a neural network. The model 206, once trained, may then be usedto process a subsequent user schedule 1106 to generate recommendations1102 based on that schedule. In this way, the model 206 may be trainedover time to make a correlation between what is worn by a user on aparticular point-in-time and an activity associated with that point intime from the user schedule 1104.

Accordingly, a subsequent user schedule 1106 (which may be acontinuation of access to the user schedule 1104) may be processed bythe model 206 of the machine learning module 204 to generate arecommendation 1002. The recommendation 1102, for instance, may describean object of clothing to be worn by a user at a future point in time,e.g., to wear a suit for an upcoming meeting. In another instance, therecommendation 1102 may describe an object of clothing for purchase by auser, such as from a commerce provider system as described in theprevious section. In a further instance, the recommendation 1102 mayalso take into account geographical considerations, such as to make arecommendation 1002 specifying “what to pack” for a vacation to aparticular location based on weather conditions predicted for thatlocation. In this way, the model 206 may richly and dynamically addressever changing situations of a user and make recommendations accordingly.

FIG. 12 depicts a procedure 1200 in an example implementation in which auser schedule and digital images are used to train a model to makerecommendations. To begin, a plurality of digital images and datadescribing times, at which, the plurality of digital images are capturedis received (block 1202). The camera platform manager module 116, forinstance, may receive digital images 114 from a digital camera 112 froma live feed in real time, from a storage device, and so on. The digitalimages 114 may have associated metadata that describes “when” respectivedigital images are captured, e.g., a timestamp.

Objects of clothing are recognized from the digital images (block 1204).An object recognition module 202, for instance, may employ machinelearning to recognize types of objects of clothing as well ascharacteristics, such as color, pattern, stature (i.e., how the objectsare worn by a user), and so forth.

A user schedule is also received that describes user appointments andtimes, at which the appointments are scheduled (block 1206). The cameraplatform manager module 116, for instance, may be given access to auser's calendar as maintained by a calendar application of the computingdevice 102.

A user profile is generated by training a model 206 using machinelearning based on the recognized objects of clothing, times at whichcorresponding digital images are captured, and the user schedule 1104(block 1208). In this way, the model 206 as shown in FIG. 11 correlatesappointments of the user with what the user wore to those appointments.

A recommendation is generated by processing a subsequent user schedule1106 using the model as part of machine learning (block 1210). Therecommendation is then output in a user interface (block 1212), e.g., aspart of the live feed using augmented reality content. Therecommendation, for instance, may suggest an object of clothing to beworn by the user that was previously worn based on the identifiedcorrelation. In another instance, the recommendation identifies anobject of clothing to be purchased by the user based on an upcomingappointment, e.g., from a commerce service provider system that makesthe object available via an auction and/or directly upon payment of aspecified amount. The outputting may be performed using AR digitalcontent, e.g., to show the object “on” the user, an accessory disposedadjacent to the user, and so forth. Other instances are alsocontemplated.

Example System and Device

FIG. 13 illustrates an example system generally at 1300 that includes anexample computing device 1302 that is representative of one or morecomputing systems and/or devices that may implement the varioustechniques described herein. This is illustrated through inclusion ofthe camera platform manager module 116. The computing device 1302 maybe, for example, a server of a service provider, a device associatedwith a client (e.g., a client device), an on-chip system, and/or anyother suitable computing device or computing system.

The example computing device 1302 as illustrated includes a processingsystem 1304, one or more computer-readable media 1306, and one or moreI/O interface 1308 that are communicatively coupled, one to another.Although not shown, the computing device 1302 may further include asystem bus or other data and command transfer system that couples thevarious components, one to another. A system bus can include any one orcombination of different bus structures, such as a memory bus or memorycontroller, a peripheral bus, a universal serial bus, and/or a processoror local bus that utilizes any of a variety of bus architectures. Avariety of other examples are also contemplated, such as control anddata lines.

The processing system 1304 is representative of functionality to performone or more operations using hardware. Accordingly, the processingsystem 1304 is illustrated as including hardware element 1310 that maybe configured as processors, functional blocks, and so forth. This mayinclude implementation in hardware as an application specific integratedcircuit or other logic device formed using one or more semiconductors.The hardware elements 1310 are not limited by the materials from whichthey are formed or the processing mechanisms employed therein. Forexample, processors may be comprised of semiconductor(s) and/ortransistors (e.g., electronic integrated circuits (ICs)). In such acontext, processor-executable instructions may beelectronically-executable instructions.

The computer-readable storage media 1306 is illustrated as includingmemory/storage 1312. The memory/storage 1312 represents memory/storagecapacity associated with one or more computer-readable media. Thememory/storage component 1312 may include volatile media (such as randomaccess memory (RAM)) and/or nonvolatile media (such as read only memory(ROM), Flash memory, optical disks, magnetic disks, and so forth). Thememory/storage component 1312 may include fixed media (e.g., RAM, ROM, afixed hard drive, and so on) as well as removable media (e.g., Flashmemory, a removable hard drive, an optical disc, and so forth). Thecomputer-readable media 1306 may be configured in a variety of otherways as further described below.

Input/output interface(s) 1308 are representative of functionality toallow a user to enter commands and information to computing device 1302,and also allow information to be presented to the user and/or othercomponents or devices using various input/output devices. Examples ofinput devices include a keyboard, a cursor control device (e.g., amouse), a microphone, a scanner, touch functionality (e.g., capacitiveor other sensors that are configured to detect physical touch), a camera(e.g., which may employ visible or non-visible wavelengths such asinfrared frequencies to recognize movement as gestures that do notinvolve touch), and so forth. Examples of output devices include adisplay device (e.g., a monitor or projector), speakers, a printer, anetwork card, tactile-response device, and so forth. Thus, the computingdevice 1302 may be configured in a variety of ways as further describedbelow to support user interaction.

Various techniques may be described herein in the general context ofsoftware, hardware elements, or program modules. Generally, such modulesinclude routines, programs, objects, elements, components, datastructures, and so forth that perform particular tasks or implementparticular abstract data types. The terms “module,” “functionality,” and“component” as used herein generally represent software, firmware,hardware, or a combination thereof. The features of the techniquesdescribed herein are platform-independent, meaning that the techniquesmay be implemented on a variety of commercial computing platforms havinga variety of processors.

An implementation of the described modules and techniques may be storedon or transmitted across some form of computer-readable media. Thecomputer-readable media may include a variety of media that may beaccessed by the computing device 1302. By way of example, and notlimitation, computer-readable media may include “computer-readablestorage media” and “computer-readable signal media.”

“Computer-readable storage media” may refer to media and/or devices thatenable persistent and/or non-transitory storage of information incontrast to mere signal transmission, carrier waves, or signals per se.Thus, computer-readable storage media refers to non-signal bearingmedia. The computer-readable storage media includes hardware such asvolatile and non-volatile, removable and non-removable media and/orstorage devices implemented in a method or technology suitable forstorage of information such as computer readable instructions, datastructures, program modules, logic elements/circuits, or other data.Examples of computer-readable storage media may include, but are notlimited to, RAM, ROM, EEPROM, flash memory or other memory technology,CD-ROM, digital versatile disks (DVD) or other optical storage, harddisks, magnetic cassettes, magnetic tape, magnetic disk storage or othermagnetic storage devices, or other storage device, tangible media, orarticle of manufacture suitable to store the desired information andwhich may be accessed by a computer.

“Computer-readable signal media” may refer to a signal-bearing mediumthat is configured to transmit instructions to the hardware of thecomputing device 1302, such as via a network. Signal media typically mayembody computer readable instructions, data structures, program modules,or other data in a modulated data signal, such as carrier waves, datasignals, or other transport mechanism. Signal media also include anyinformation delivery media. The term “modulated data signal” means asignal that has one or more of its characteristics set or changed insuch a manner as to encode information in the signal. By way of example,and not limitation, communication media include wired media such as awired network or direct-wired connection, and wireless media such asacoustic, RF, infrared, and other wireless media.

As previously described, hardware elements 1310 and computer-readablemedia 1306 are representative of modules, programmable device logicand/or fixed device logic implemented in a hardware form that may beemployed in some embodiments to implement at least some aspects of thetechniques described herein, such as to perform one or moreinstructions. Hardware may include components of an integrated circuitor on-chip system, an application-specific integrated circuit (ASIC), afield-programmable gate array (FPGA), a complex programmable logicdevice (CPLD), and other implementations in silicon or other hardware.In this context, hardware may operate as a processing device thatperforms program tasks defined by instructions and/or logic embodied bythe hardware as well as a hardware utilized to store instructions forexecution, e.g., the computer-readable storage media describedpreviously.

Combinations of the foregoing may also be employed to implement varioustechniques described herein. Accordingly, software, hardware, orexecutable modules may be implemented as one or more instructions and/orlogic embodied on some form of computer-readable storage media and/or byone or more hardware elements 1310. The computing device 1302 may beconfigured to implement particular instructions and/or functionscorresponding to the software and/or hardware modules. Accordingly,implementation of a module that is executable by the computing device1302 as software may be achieved at least partially in hardware, e.g.,through use of computer-readable storage media and/or hardware elements1310 of the processing system 1304. The instructions and/or functionsmay be executable/operable by one or more articles of manufacture (forexample, one or more computing devices 1302 and/or processing systems1304) to implement techniques, modules, and examples described herein.

The techniques described herein may be supported by variousconfigurations of the computing device 1302 and are not limited to thespecific examples of the techniques described herein. This functionalitymay also be implemented all or in part through use of a distributedsystem, such as over a “cloud” 1314 via a platform 1316 as describedbelow.

The cloud 1314 includes and/or is representative of a platform 1316 forresources 1318. The platform 1316 abstracts underlying functionality ofhardware (e.g., servers) and software resources of the cloud 1314. Theresources 1318 may include applications and/or data that can be utilizedwhile computer processing is executed on servers that are remote fromthe computing device 1302. Resources 1318 can also include servicesprovided over the Internet and/or through a subscriber network, such asa cellular or Wi-Fi network.

The platform 1316 may abstract resources and functions to connect thecomputing device 1302 with other computing devices. The platform 1316may also serve to abstract scaling of resources to provide acorresponding level of scale to encountered demand for the resources1318 that are implemented via the platform 1316. Accordingly, in aninterconnected device embodiment, implementation of functionalitydescribed herein may be distributed throughout the system 1300. Forexample, the functionality may be implemented in part on the computingdevice 1302 as well as via the platform 1316 that abstracts thefunctionality of the cloud 1314.

CONCLUSION

Although the invention has been described in language specific tostructural features and/or methodological acts, it is to be understoodthat the invention defined in the appended claims is not necessarilylimited to the specific features or acts described. Rather, the specificfeatures and acts are disclosed as example forms of implementing theclaimed invention.

What is claimed is:
 1. A computing device comprising: a processing system; and a computer-readable storage medium having instructions stored thereon that, responsive to execution by the processing system, causes the processing system to perform operations comprising: receiving at least one digital image; recognizing an object included within the at least one digital image; generating object recognition data based on the recognized object; generating user profile data for a user profile, the user profile data based at least in part on the generated object recognition data; generating a recommendation based on the user profile; and outputting the recommendation in a user interface.
 2. The computing device as described in claim 1, wherein the recommendation is output as augmented reality content.
 3. The computing device as described in claim 1, wherein the recommendation is a price to sell the recognized object.
 4. The computing device as described in claim 3, wherein the price to sell the recognized object is based on a plurality of online auctions involving objects similar to the recognized object.
 5. The computing device as described in claim 2, wherein the augmented reality content is output in the user interface along with a direct view of a physical environment of the computing device.
 6. The computing device as described in claim 5, wherein the direct view is part of a live feed.
 7. The computing device as described in claim 1, wherein the recommendation identifies an additional object.
 8. A method implemented by a computing device, the method comprising: receiving, by the computing device, at least one digital image; recognizing, by the computing device, an object included within the at least one digital image; generating, by the computing device, object recognition data based on the recognized object; generating, by the computing device, user profile data for a user profile, the user profile data based at least in part on the generated object recognition data; generating, by the computing device, a recommendation based on the user profile; and outputting, by the computing device, the recommendation in a user interface.
 9. The method as described in claim 8, wherein the recommendation is output as augmented reality content.
 10. The method as described in claim 9, wherein the augmented reality content is output in the user interface along with a direct view of a physical environment of the computing device.
 11. The method as described in claim 10, wherein the direct view is part of a live feed.
 12. The method as described in claim 8, wherein the recommendation identifies an additional object.
 13. The method as described in claim 12, wherein the recommendation that identifies the additional object is selectable via the user interface to purchase the additional object from a service provider system via a network.
 14. A method implemented by a computing device, the method comprising: receiving, by the computing device, at least one digital image; recognizing, by the computing device, an object from the at least one digital image, the recognizing performed using object recognition as part of machine learning; generating, by the computing device, a user profile by training a model using machine learning based on the object recognized from the at least one digital image; generating, by the computing device, a recommendation based on the generated user profile; and outputting, by the computing device, the recommendation in a user interface.
 15. The method as described in claim 14, wherein the recommendation is output as augmented reality content.
 16. The method as described in claim 15, wherein the augmented reality content is output in the user interface along with a direct view of a physical environment of the computing device.
 17. The method as described in claim 16, wherein the direct view is part of a live feed.
 18. The method as described in claim 14, wherein the recommendation identifies an additional object.
 19. The method as described in claim 18, wherein the object is selectable via the user interface for purchase from a service provider system via a network.
 20. The method as described in claim 18, wherein the object is available via an online auction. 